density and dimensionality
Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds
The study of point cloud data sampled from a stratification, a collection of manifolds with possible different dimensions, is pursued in this paper. We present a technique for simultaneously soft clustering and estimating the mixed dimensionality and density of such structures. The framework is based on a maximum likelihood estimation of a Poisson mixture model. The presentation of the approach is completed with artificial and real examples demonstrating the importance of extending manifold learning to stratification learning.
Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds
Haro, Gloria, Randall, Gregory, Sapiro, Guillermo
The study of point cloud data sampled from a stratification, a collection of manifolds with possible different dimensions, is pursued in this paper. We present a technique for simultaneously soft clustering and estimating the mixed dimensionality and density of such structures. The framework is based on a maximum likelihood estimation of a Poisson mixture model. The presentation of the approach is completed with artificial and real examples demonstrating the importance of extending manifold learning to stratification learning.
Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds
Haro, Gloria, Randall, Gregory, Sapiro, Guillermo
The study of point cloud data sampled from a stratification, a collection of manifolds with possible different dimensions, is pursued in this paper. We present a technique for simultaneously soft clustering and estimating the mixed dimensionality and density of such structures. The framework is based on a maximum likelihood estimation of a Poisson mixture model. The presentation of the approach is completed with artificial and real examples demonstrating the importance of extending manifold learning to stratification learning.
Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds
Haro, Gloria, Randall, Gregory, Sapiro, Guillermo
The study of point cloud data sampled from a stratification, a collection of manifolds withpossible different dimensions, is pursued in this paper. We present a technique for simultaneously soft clustering and estimating the mixed dimensionality anddensity of such structures. The framework is based on a maximum likelihood estimationof a Poisson mixture model. The presentation of the approach is completed with artificial and real examples demonstrating the importance of extending manifold learning to stratification learning.